The traditional multi-dimension assignment data association algorithm for the multi-passive-sensor data association algorithm has ignored the errors introduced by location localization estimation. Therefore, a data association algorithm is proposed based on information divergence. The differentia between the probability density function of pseudo measurements and the most posterior probability density function works as the association cost. The Kullback-Leibler divergence and symmetric Kullback-Leibler divergence are used respectively to quantify the differentia above. Finally, simulation results show that the proposed algorithm can achieve better performance and its association cost reflects the association probability more accurately.